
Lexical Features (LF):

Orthographic Form (OF): We abstract OF features from the actual word and obtain its type (i.e., numeric, alpha, alphanumeric, or other). Additionally, we classify the word as: all upper, all lower, title case, or mixed case.

Word Shape (WS): We define WS to abstract the patterns of letters in a word as a short/long word shape (SWS/LWS) features. In LWS, we map each letter to “L”, each digit to “D”, and retain the others. On the other hand, for SWS, we remove consecutive character types. For example, LWS(ABC-123) \(\rightarrow \)  “LLL-DDD” and SWS(ABC-123) \(\rightarrow \)  “L-D”.


Lexico-Syntactic Features (LS):
We use a skip-gram method to record the explicit LS-patterns surrounding each NP, i.e., for each \(np\)  in \(s\)  we record the pattern \(w_{i-1}+np+w_{i+1}\) , where \(w_{(.)}\)  are the two words that precede and follow \(np\) .

Syntactic Features (SF):
We use dependency patterns to abstract away from the word-order information that we can capture using the contextual and lexico-syntactic features. We use Stanford's English_UD neural network-based dependency parser [1]} to extract universal dependencies of NPs. For each \(np\)  in \(s\) , we record two dependency patterns of the NP that serve in governor and dependent roles.

Semantic Features (SeF):
To capture the lexical semantics, we use WordNet [2]} to get word senses and draw sense relations between NPs if they have the same sense class.

Contextual Features (CF):
To capture the latent features, we use a Word2Vec embedding model [3]} trained on the sentence pool \(S\)  as the only bottom-up distributional semantics method. We exploit the word-context co-occurrence patterns learned by the model to induce the relational similarities between NPs.

